DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle Glossary Bibliography Index

Elements

> letters		        # a b c [...] z
> letters[10]			# "j"
> letters[10:15]		# "j" "k" "l" "m" "n" "o"
> letters[c(1, 2, 4, 8, 16)]	# "a" "b" "d" "h" "p"
> letters[-(10:26)]		# "a" "b" "c" "d" "e" "f" "g" "h" "i"

An operator (or function) can be applied to a vector to return a vector. This is particularly useful for boolean operators, returning a vector of boolean values which can then be used to select specific elements of a vector:

> letters > "j"		        		# FALSE FALSE FALSE [...] TRUE
> letters[letters > "j"]	        	# "k" "l" "m" "n" [...] "y" "z"
> letters[letters > "w" | letters < "e"]	# "a" "b" "c" "d" "x" "y" "z"

Here's a useful trick to ensure we don't divide by zero, which would otherwise give an infinite answer (XnullXXnullXR variablesR functions (R function)R variablesR libraries (R library)R variablesR option (R option)R variablesR packages (R package)R variablesDatasets (Dataset)XnullXR variablesR variablesInf):

> x <- c(0.28, 0.55, 0, 2)
> y <- c(0.53, 1.34, 1.2, 2.07)
> sum(((x-y)^2/x))                  
[1] Inf
> sum(((x-y)^2/x)[x!=0])            # Exclude the zeros
[1] 1.360392

We could also generate random subsets of our data.

> subdataset <- dataset[sample(seq(1, nrow(dataset)), 1000),]

We can select elements meeting set inclusion conditions. Here we first select a subset of rows from a data frame having particular colours.

> ds[ds$colour %in% c("green", "blue"),]
> ds[ds$colour %in% names(which(table(ds$colour) > 11)),]

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